





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @slow
     @require_torch
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"      ,   "prajjwal1/bert-tiny"	)
        A_       :					List[Any]        =      BertTokenizer.from_pretrained("bert-base-uncased"	)

        A_       :					Union[str, Any]        =      bertabert.config.encoder.vocab_size
        A_       :					List[Any]        =      tokenizer.sep_token_id
        A_       :					Tuple        =      tokenizer.cls_token_id
        A_       :					List[Any]        =      128

        A_       :					Optional[int]        =      datasets.load_dataset("cnn_dailymail"      ,   "3.0.0"      ,   split="train[:1%]"	)
        A_       :					Optional[int]        =      datasets.load_dataset("cnn_dailymail"      ,   "3.0.0"      ,   split="validation[:1%]"	)

        A_       :					str        =      train_dataset.select(range(32	)	)
        A_       :					Tuple        =      val_dataset.select(range(16	)	)

        A_       :					str        =      4

        def _map_to_encoder_decoder_inputs(snake_case							:Tuple	):
           # Tokenizer will automatically set [BOS] <text> [EOS]
           A_       :					List[Any]        =      tokenizer(batch["article"]      ,   padding="max_length"      ,   truncation=lowercase_      ,   max_length=512	)
           A_       :					List[Any]        =      tokenizer(batch["highlights"]      ,   padding="max_length"      ,   truncation=lowercase_      ,   max_length=128	)
           A_       :					Union[str, Any]        =      inputs.input_ids
           A_       :					Optional[Any]        =      inputs.attention_mask

           A_       :					Tuple        =      outputs.input_ids
           A_       :					Optional[int]        =      outputs.input_ids.copy()
           A_       :					List[Any]        =      [
               [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
           ]
           A_       :					int        =      outputs.attention_mask

           assert all(len(lowercase_	) == 512 for x in inputs.input_ids	)
           assert all(len(lowercase_	) == 128 for x in outputs.input_ids	)

           return batch

        def _compute_metrics(snake_case							:Optional[Any]	):
           A_       :					Any        =      pred.label_ids
           A_       :					Union[str, Any]        =      pred.predictions

           # all unnecessary tokens are removed
           A_       :					Dict        =      tokenizer.batch_decode(lowercase_      ,   skip_special_tokens=lowercase_	)
           A_       :					Optional[int]        =      tokenizer.batch_decode(lowercase_      ,   skip_special_tokens=lowercase_	)

           A_       :					Tuple        =      sum([int(pred_str[i] == label_str[i]	) for i in range(len(lowercase_	)	)]	) / len(lowercase_	)

           return {"accuracy": accuracy}

        # map train dataset
        A_       :					Dict        =      train_dataset.map(
            _map_to_encoder_decoder_inputs      ,   batched=lowercase_      ,   batch_size=lowercase_      ,   remove_columns=["article", "highlights"]      ,   )
        train_dataset.set_format(
            type="torch"      ,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]      ,   )

        # same for validation dataset
        A_       :					Union[str, Any]        =      val_dataset.map(
            _map_to_encoder_decoder_inputs      ,   batched=lowercase_      ,   batch_size=lowercase_      ,   remove_columns=["article", "highlights"]      ,   )
        val_dataset.set_format(
            type="torch"      ,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]      ,   )

        A_       :					Optional[int]        =      self.get_auto_remove_tmp_dir()

        A_       :					int        =      SeqaSeqTrainingArguments(
            output_dir=lowercase_      ,   per_device_train_batch_size=lowercase_      ,   per_device_eval_batch_size=lowercase_      ,   predict_with_generate=lowercase_      ,   evaluation_strategy="steps"      ,   do_train=lowercase_      ,   do_eval=lowercase_      ,   warmup_steps=0      ,   eval_steps=2      ,   logging_steps=2      ,   )

        # instantiate trainer
        A_       :					str        =      SeqaSeqTrainer(
            model=lowercase_      ,   args=lowercase_      ,   compute_metrics=_compute_metrics      ,   train_dataset=lowercase_      ,   eval_dataset=lowercase_      ,   tokenizer=lowercase_      ,   )

        # start training
        trainer.train()

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    3
   _UpperCamelCase           =    250

   _UpperCamelCase           =    ids_tensor((batch_size, length)							, lowercase_)
   _UpperCamelCase           =    torch.ones((batch_size, length)							, device=lowercase_							, dtype=torch.float) / length
   return input_ids, scores







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10),
           MaxTimeCriteria(max_time=0.1),
       ])

   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxLengthCriteria(max_length=10)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxNewTokensCriteria(start_length=5							, max_new_tokens=5)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    StoppingCriteriaList([criteria])
   self.assertEqual(criteria_list.max_length							, 10)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1							, initial_timestamp=time.time() - 0.2)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 10)

   with self.assertWarns(lowercase_):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 11)

   _UpperCamelCase           =    validate_stopping_criteria(StoppingCriteriaList()							, 11)

   self.assertEqual(len(lowercase_)							, 1)




